Generating Multimodal Images with GAN: Integrating Text, Image, and Style
Journal:
arXiv
Published Date:
Jan 4, 2025
Abstract
In the field of computer vision, multimodal image generation has become a
research hotspot, especially the task of integrating text, image, and style. In
this study, we propose a multimodal image generation method based on Generative
Adversarial Networks (GAN), capable of effectively combining text descriptions,
reference images, and style information to generate images that meet multimodal
requirements. This method involves the design of a text encoder, an image
feature extractor, and a style integration module, ensuring that the generated
images maintain high quality in terms of visual content and style consistency.
We also introduce multiple loss functions, including adversarial loss,
text-image consistency loss, and style matching loss, to optimize the
generation process. Experimental results show that our method produces images
with high clarity and consistency across multiple public datasets,
demonstrating significant performance improvements compared to existing
methods. The outcomes of this study provide new insights into multimodal image
generation and present broad application prospects.
